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Narrative Analysis in Injury
Research
Thomas Songer, PhD
This injury was
University of Pittsburgh due to …….
tjs@pitt.edu
Large datasets are used very frequently in injury research and injury control
initiatives. This lecture introduces the concept of narrative text analysis and its use
and role in injury epidemiology. Upon completing the lecture, the reader should be
able to:
1. Describe the application of narrative text analysis in the injury field
2. Understand the limitations of this form of analysis
This lecture is based upon recent publications that detail the use of narrative text
analysis to assess occupational injuries. For examples of narrative analysis in
practice in the injury field, see the following papers:
-Jones SJ, Lyons RA. Routine narrative analysis as a screening tool to improve data
quality. Injury Prevention 9:184-186, 2003.
-Williamson A, Feyer AM, Stout N, Driscoll T, Usher H. Use of narrative analysis
for comparisons of the causes of fatal accidents in three countries; New Zealand,
Australia, and the United States. Injury Prevention 7(Suppl I):i15-20.
-Wellman HM, Lehto MR, Sorock GS, Smith GS. Computerized coding of injury
narrative data from the National Health Interview Survey. Accident Analysis &
Prevention 36:165-171, 2004.
1
Aims
• Recognize the value of narrative data that
accompanies coded data
• Provide a framework for exploring
narratives
• Ultimate Goal: see to better understand
circumstances of an injury
There are two primary objectives to this lecture. They include (1) having the ability
to recognize the value that narrative data add to large existing datasets, and (2)
understanding the foundation or framework that one can use in exploring narrative
data.
Remember that the ultimate goal in this whole process is to use data to better
understand the circumstances surrounding an injury.
2
Qualitative Data
• Data in the form of words, language in the
form of extended text
• Can be an alternative research strategy
• The underlying assumptions, methods, and
research procedures of qualitative
approaches are often very different from
quantitative approaches
Thomas DR
Narrative analysis is one form of qualitative data analysis. Qualitative data are data
in the form of words rather than numbers. Research designs, methods, and analytic
approaches for studies that collect qualitative data are quite different from studies
that rely upon quantitative data.
3
Qualitative Research Approaches
• Phenomenology
• Ethnography
• Narrative analysis
• Grounded theory
• General inductive
• Discourse analysis
• Case studies
Thomas DR
Examples of qualitative research approaches are shown here. Narrative analysis is
used frequently in the social sciences, particular in the discipline of anthropology.
Sources of data in narrative analysis studies are wide ranging, but often include case
studies, existing documents (such as archives and records), forms of media
(newspaper, audio accounts), etc.
4
Comparison of descriptive ability:
Fx ankle running on base after dark; stepped in pothole
Domain Data Type Nature of injury (N) External cause (E)
824.1 239
Medical ICD codes (N) Fx of ankle--Medial Athletics/sports incl
(inpatient) STANAG (E) malleolus, open PT, other
Medical 824.1
ICD codes (N) Fx of ankle--Medial Not available
(outpatient) malleolus, open
Hierarchical Inj Type = Fracture Activity = Running
Mechanism* = Pothole
Safety taxonomy & Body Part = Foot/ Time = 2200 hrs
narrative text ankle On_Base = YES
Narrative: “Jogging on
running trail, detoured
onto Center St due to mud .
. . .”
* Derived from narrative
So what is the role of narrative analysis in injury research?
This slide illustrates why narrative data have gathered the interest of injury
epidemiologists. The use of narrative data has been advocated primarily because of
the existing limitations of studying injuries in medical data systems. In these
systems, injuries are identified by N-codes which describe the nature of the injury
(e.g. tibial fracture) and E-codes which describe the main external factor which
caused the injury (e.g. motor vehicle accident). This type of classification tends to
leave out a lot of the details surrounding an injury. Knowledge of the details may
lead to the development of better prevention initiatives.
Consider the example illustrated here. This represents a person who got a fractured
ankle from running on a military base after dark and stepping in a hole in the road.
If you consider the fundamental data in the medical record to identify injuries, you
will only learn that the person has a fractured ankle (N-code) due to athletics (E-
code or STANAG code). This is if the person is hospitalized. If they are seen in an
emergency department, it is very likely that there will not be any E-code listed.
Thus, you have very limited data that detail the circumstances of the injury.
Narrative data provide more details on the injury, and in this case the narrative text
identifies the activity (running), the time (2200 hours), and how the injury occurred
(stepped in a hole on the road).
5
Text data accompanies
computerized coded data
029801340982110000 TWISTED ANKLE WHILE WALKING
888015690134234520
112952589301300000 POSSIBLE FOOD POISONING
111436012045021439
210001214234131980
888121200012314521
013298231343287821 WOUNDED BY GUNSHOT
977237211010132123
012128881234100010 FELL AND STRUCK BED
Because of the limitations of existing medical data systems, many injury
epidemiologists now advocate that a short narrative text that details the
circumstances of the injury should be included in medical data in addition to the
information already contained within them.
Some surveillance systems are now including narrative text to supplement existing
computerized data for research purposes. An example of data that appear in the
National Hospital Ambulatory Medical Care Survey (NHAMCS) is shown here.
Data fields exist that identify the attributes related to the medical visits. In addition,
there is also a data field of narrative text included for all injuries in the NHAMCS
system.
Note how this example incorporates both quantitative and qualitative data together.
The primary research design, however, is quantitative in focus. It does not follow
the typical methods and design issues seen in qualitative studies. This is one
limitation that is inherent in narrative data added to existing data systems. This
issue will be discussed shortly.
6
Analytic Approaches
• Computerized keyword search using
automated coding software
• manual keyword search
• manual review
– create new variables
– recategorize data
– code into categories
– identify summary themes
So, how do you analyze narrative data? No one standard exists for examining and
assessing narrative data. There are, in fact, several approaches that one can take.
You can first go through the text data in varying formats. This includes reviewing
each record manually (very time intensive), reviewing records with keyword
searches, or buying automated coding software to conduct a keyword search
(potentially expensive).
The broad goal of this review is to identify themes or common occurrences in the
data. The researcher can then code or categorize these themes into quantitative
data.
7
Developing Categories for Analysis
• Initial categories should be determined by
the objectives of the research
• Specific categories can be developed from
the review of the data and the identification
of frequent or significant themes, words,
circumstances
Thomas DR
The categories that are developed for analysis are often considered from two
perspectives. First, researchers should develop hypotheses based upon the research
objectives of the study. These hypotheses will illustrate categories that a researcher
should initially identify in reviewing the data. For example, consider the hypothesis
that a specific type of equipment is involved in occupational injuries. The initial
categories developed for the analysis will include this type of equipment and its
variations.
The second perspective is to develop categories after reviewing the data. Categories
can be developed based upon the frequency in which particular themes, words, or
phrases appear in the data.
8
Data Consistency
• Identify a “case definition” or “eligibility
criteria”
• Go through data several times to check
consistency of coding applied to the
categories
• Consider the use of an independent coder
In developing categories for analysis, it is extremely important to identify a case
definition or eligibility criteria to apply when reviewing the data. This will help to
ensure that the data are consistent, or that the criteria that make an event a case are
applied evenly throughout the study. When reviewing narrative text, researchers
often find that they need to amend the eligibility criteria for a category. This is
because they read of a differing circumstance that was not previously considered.
Thus, it is important in this situation to review all of the records again to apply the
eligibility criteria in the same fashion to each record.
9
An Alternative Approach
• Analysis of narrative text structure
– setting
– individuals involved
– problem
– initiating event
– 2nd event
– 3rd event
– 4th event
– resolution
As mentioned before, there is no gold standard approach to the analysis of narrative
text data. A researcher does not always have to form hypotheses first, or rely upon
keyword frequencies. Another approach to starting a narrative analysis is illustrated
here. This approach is taken from assessment of writing in general. In this
approach, each record is reviewed to identify the listed criteria. For example, for
each record, the text is reviewed to identify the setting, who was involved, the
problem (e.g. injury), the event that initiated the problem, and how the problem was
resolved. In this approach, the researcher is creating narrative data from narrative
data.
This approach provides a framework to classifying a vast array of information. The
researcher can then develop coding categories on the basis of this classification.
One has to be cautious here, though, because the manner in which the original text
is classified can vary from researcher to researcher. In this sense, the potential
exists for the results of a narrative analysis to vary from researcher to researcher.
10
Limitations
• No guidelines for what is entered as text
• Manual review is time intensive
• Purpose of data collection may not mirror
underlying needs of injury research
Additional limitations exist in narrative text analysis as well, particularly with
respect to its application in injury research. One of the largest concerns is in the
methods applied to record narrative text. For many data systems, there are no
guidelines given for what information is recorded as narrative text, and where this
information is gathered from in the record. Different individuals involved in data
entry may input different text. Further, the reasons why narrative text are entered
into data systems may not have anything to do with injury research. In this
situation, it is not possible to determine if the circumstances of the injury are not
listed in the dataset because they were not in the record, or because they were in the
record, but were not entered.
Another area of concern lies in the review process itself. We have previously
discussed the need for consistency in the data categories developed in narrative
data. However, narrative analysis is also a very time-intensive analytic procedure.
Most studies are based upon manual review of narrative data. Reviewing hundreds
or thousands of records and documenting data categories in this fashion requires a
lot of time.
11
Key Lecture Points
• Narrative analysis is another research
technique in the injury field.
• It is advocated in situations where existing
data contain very little information on the
circumstances surrounding an injury.
• The analytic approaches in narrative
analysis are often subjective, and great care
must be taken to develop data consistency
to support the results of the analysis.
12
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